DeepSeek vs. ChatGPT: Comparative Efficacy in Reasoning for Adults’ Second Language Acquisition Analysis

Abstract

The advent of new generation language models has revolutionized the field of natural language processing (NLP) due to their exceptional understanding and human language generation capabilities. ChatGPT emerged as an essential model with remarkable strengths for various applications. DeepSeek has recently emerged as the latest advancement in NLP, showing great potential in pure text-generation jobs, semantic analysis, and context-dependent language modeling capabilities. The study investigates and compares the performance of DeepSeek and ChatGPT in assessing adult L2 (second language) acquisition errors applied primarily to South Asian Arabic learners. With this premise, we aim to evaluate their efficacy in detecting linguistic inaccuracies (morphology, syntax, semantics) and diagnosing cases of L1 (first language). Methods include error analysis of non-native Arabic sentences, comparative evaluation of the two models, and contrasting assessment of depth of reasoning. Results show that DeepSeek was significantly better at context-driven error detection (for instance, in detecting SOV word-order transfer), and ChatGPT presented more instructively relevant feedback. However, both needed fine-tuned prompts to bring in feedback related to semantic/pragmatic errors, such as missing articles and dialectal mismatches. Contributions include a proposal for integrating AI tools into L2 pedagogy, emphasizing contrastive drill and sociolinguistic awareness, and recommendations for training AI concerning L1-targeted error profiles. This research pushes the integration of AI into language instruction for scalable solutions for adult L2 learners while pointing at improvements needed in the models.

Keywords

• DeepSeek
• ChatGPT
• LLMs
• Mother Tongue Influence (MTI)
• Second Language Acquisition (SLA)
• AI-Assisted Error Detection